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Meeting MS&T26: Materials Science & Technology
Symposium Additive Manufacturing: Equipment, Instrumentation and In-Situ Process Monitoring
Presentation Title Hybrid Feedforward-Feedback Process Control of Laser Powder Bed Fusion
Author(s) Prahalada K. Rao, Kaustubh Deshmukh, Mihir Darji, Yash Parikh
On-Site Speaker (Planned) Prahalada K. Rao
Abstract Scope The objective is to develop and implement a multi-scale, dual-mode hybrid feedforward–feedback control approach to modulate the spatiotemporal temperature distribution (thermal history) in LPBF-processed components. Existing control approaches in LPBF are inherently limited because they focus on one process scale and employ a single control mode. For example, model-guided feedforward control is typically used to correct deleterious part-scale thermal fields before manufacturing. Likewise, real-time feedback control has been implemented to correct meltpool-scale temperature variations. Feedforward control cannot compensate for stochastic disturbances, while feedback control lacks predictive capability and suffers from response latency. The hybrid process control approach implemented in this paper integrates a priori model-based feedforward control of part-scale thermal history with real-time feedback control meltpool intensity. It is employed herein for LPBF of Inconel 718 parts. Compared to purely feedback or feedforward control, hybrid control significantly reduces distortion and mitigates within-part variation in surface finish and grain structure.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

AMDiffusion: Domain-Adaptive Diffusion Modeling for Causal Data Fusion in Additive Manufacturing
Beyond Deep Learning: A Bayesian-Optimized Computer Vision Framework for Rapid Spatter Detection and Tracking in Laser Powder Bed Fusion
Designing Sensor Systems for Anomaly and Flaw Detection in Laser Powder Bed Fusion Additive Manufacturing
Hybrid Feedforward-Feedback Process Control of Laser Powder Bed Fusion
K2: An Open Architecture Wire-Laser Directed Energy Deposition Testbed for Advanced Control Strategy Development
Large Language Models for In-Situ Interpretation of Defect Signatures in Powder Bed Fusion
Rapid Modeling and Prediction of Thermal Strain in Laser Powder Bed Fusion
Self-Sensing of 3D-Printed Materials by Measuring the Inductance, Resistance and Capacitance
Smoke, Mirrors, and Melt Pools: An Assessment of Commercial PBF-LB In-Situ Process Monitoring Solutions

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